| Abstract
| - In this work, solution strategies for the optimal design of nonredundant observable linear sensornetworks are discussed. The Greedy algorithm allows the problem only to be tackled for a subsetof optimization criteria. Particular deterministic techniques or general evolutionary strategiesare necessary to solve the problem for more complex objective functions. In this context, aprocedure based on the application of genetic algorithms (GAs) and linear algebra is presented.Ad hoc operators are designed for the crossover and mutation operations because the classicgenetic operators perform poorly. In contrast to ad hoc deterministic codes, which find the designsolution for each specific criteria, this strategy allows the problem to be solved with differentobjective functions using the same implementation. Furthermore, this code is extended to handlemultiobjective problems through a modification of only the selection operator. An industrialexample is provided to show the efficiency of the algorithm.
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